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This means that for each label, we go through the items in the feature set and we add the log probability of each item to logprodlabel.The post also describes the internals of NLTK related to this implementation.Even with those numbers, it is quite a small sample and you should use a much larger set if you want good results.First element is an array containing the words and second element is the type of sentiment.
We get rid of the words smaller than 2 characters and we use lowercase for everything. It is a list with every distinct words ordered by frequency of appearance. To Sentiment Classifier Nltk Numpy Sentiwordnet In Anaconda Prompt Plus The TwoWe use the following function to get the list plus the two helper functions. The one we are going to use returns a dictionary indicating what words are contained in the input passed. We use the word features list defined above along with the input to create the dictionary. We obtain the following dictionary which indicates that the document contains the words: love, this and car. We pass the feature extractor along with the tweets list defined above. It is a list of tuples which each tuple containing the feature dictionary and the sentiment string for each tweet. In our case, the frequency of each label is the same for positive and negative. The word amazing appears in 1 of 5 of the positive tweets and none of the negative tweets. ![]() Those two probability objects are used to create the classifier. We can see that the probability for the input to be negative is about 0.077 when the input contains the word best. Here, we see that if the input does not contain the word not then the positive ration is 1.6. Our classifier is able to detect that this tweet has a positive sentiment because of the word friend which is associated to the positive tweet He is my best friend. What we pass to the classify method is the feature set of the tweet we want to analyze. The feature set dictionary indicates that the tweet contains the word friend. The parameter passed to the method classify is the feature set dictionary we saw above. ![]() ![]() The probability of each label (positive and negative) is 0.5. The log probability is the log base 2 of that which is -1.
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